Proposal and implementation of k-anonymization method for data insertion and deletion
Keiten Han, Hiroaki Nishi · 2023
Preserving the privacy of individuals while publishing their relevant data has been an important issue. K-anonymization, which is one of the most useful privacy models, is valid for static data; however, it does not support dynamic data updating. In this paper, we propose a method for inserting and deleting data into and from k-anonymous tabular data while maintaining k-anonymity. This method ensures k-anonymity after multiple publications by retaining the raw data and the latest published data. In the deletion process, the specified record is first deleted from the data table, and the records remaining in the q*-block to which the specified record belonged are inserted into the data table excluding the remaining records. This insertion of multiple records is performed by repeating the above addition process in sequence.